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Idea mining is a new research topic which is gaining momentous attention among knowledge engineering research community. It aims to extend the benefits of the information retrieval by sifting through historical documents so that valuable machine-proposed ideas can be extracted. This paper is mainly focusing on the evaluation of candidate ideas generated by idea mining systems. The main challenge faced by judges is how to evaluate the extracted ideas. Different evaluation methods are critically explored, and evaluation criteria are proposed accordingly. The results showed that the Likert scale measurement is more reliable than binary scale measurement.
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